Systems and methods for assessing user physiology based on eye tracking data

ABSTRACT

Systems and methods are disclosed for assessing user physiology via eye tracking data. One method includes determining a plurality of visual assessments; determining, for each assessment of the plurality of visual assessments, a data schema, where at least one data schema is associated with more than one assessment of the plurality of visual assessments; storing the determined data schema; receiving user eye tracking data associated with a selected visual assessment of the plurality of visual assessments; determining, of the stored data schema (a), a selected stored data schema associated with the selected visual assessment; categorizing the received eye tracking data based on the selected stored data schema; computing quantitative data based on the categorized data and data related to one or more individuals other than the user; generating a report of user physiological function based on the computed quantitative data; and outputting the report to a web portal.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.62/627,603 filed Feb. 7, 2018 and U.S. Provisional Application No.62/654,513 filed Apr. 8, 2018, the entire disclosures of which arehereby incorporated herein by reference in their entireties.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally toevaluating, assessing, and improving human physiological performanceusing eye tracking data. More specifically, exemplary embodiments of thepresent disclosure relate to a cloud-based platform that processes eyetracking data to provide assessments and recommendations related to theuser's physical or neurological function.

BACKGROUND

Visual assessments, consumer skill optimization, and medical evaluationsare currently offered through disparate channels. Basic eye exams forcommon eye issues are available at optometrist sites and increasinglyvia mobile applications. Lifestyle-oriented mobile applications may alsohelp consumers seeking to optimize their capabilities, in readingfaster, becoming better athletes, elevating their performance at theirjobs, etc. Further, medical facilities and schools are facing increasingdemand for noninvasive, preemptive, and accurate medical tests, e.g.,for attention-deficit/hyperactivity disorder, Parkinson's disease,depression, dementia, rehabilitation, etc. Current visual assessmentsaddress only basic, rudimentary eye issues (e.g., myopia), rather thanthe skills optimization sought by consumers or the medical evaluationssought by medical patients. Also, consumers seeking to optimize theirabilities partake in assessments and exercises that differ from thetests that medical patients undergo.

Accordingly, a need exists for systems and methods for providing a rangeof assessments and training options (e.g., for skills optimization ormedical treatment) via a single platform.

SUMMARY

According to certain embodiments, methods are disclosed for assessinguser physiology via eye tracking data. One method includes determining aplurality of visual assessments; determining, for each assessment of theplurality of visual assessments, a data schema, where at least one dataschema is associated with more than one assessment of the plurality ofvisual assessments; storing the determined data schema; receiving usereye tracking data associated with a selected visual assessment of theplurality of visual assessments; determining, of the stored data schema(a), a selected stored data schema associated with the selected visualassessment; categorizing the received eye tracking data based on theselected stored data schema; computing quantitative data based on thecategorized data and data related to one or more individuals other thanthe user; generating a report of user physiological function based onthe computed quantitative data; and outputting the report to a webportal.

According to certain embodiments, systems are disclosed for hosting oneor more visual assessments. One system includes a data storage devicestoring instructions for assessing user physiology via eye trackingdata; and a processor configured to execute the instructions to performa method including: determining a plurality of visual assessments;determining, for each assessment of the plurality of visual assessments,a data schema, where at least one data schema is associated with morethan one assessment of the plurality of visual assessments; storing thedetermined data schema; receiving user eye tracking data associated witha selected visual assessment of the plurality of visual assessments;determining, of the stored data schema (a), a selected stored dataschema associated with the selected visual assessment; categorizing thereceived eye tracking data based on the selected stored data schema;computing quantitative data based on the categorized data and datarelated to one or more individuals other than the user; generating areport of user physiological function based on the computed quantitativedata; and outputting the report to a web portal.

According to certain embodiments, a computer readable medium isdisclosed storing instructions that, when executed by a computer, causethe computer to perform a method of assessing user physiology via eyetracking data, the method including determining a plurality of visualassessments; determining, for each assessment of the plurality of visualassessments, a data schema, where at least one data schema is associatedwith more than one assessment of the plurality of visual assessments;storing the determined data schema; receiving user eye tracking dataassociated with a selected visual assessment of the plurality of visualassessments; determining, of the stored data schema (a), a selectedstored data schema associated with the selected visual assessment;categorizing the received eye tracking data based on the selected storeddata schema; computing quantitative data based on the categorized dataand data related to one or more individuals other than the user;generating a report of user physiological function based on the computedquantitative data; and outputting the report to a web portal.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary physiological assessmentsystem that uses eye tracking data, according to an exemplary embodimentof the present disclosure;

FIG. 2A is a flow diagram of an exemplary method for preparing raw eyetracking data to assess a user's physiology, according to an exemplaryembodiment of the present disclosure;

FIG. 2B is a flow diagram of an exemplary method for computing metricsfor an user's physiological assessment based on processed eye trackingdata, according to an exemplary embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

Although visual assessments are widely available, the visual assessmentsoften evaluate only common eye issues. Recent consumer trends alsoemphasize optimization of capabilities (e.g., improved reading speed orcomprehension, athletic ability, work performance, etc.) andnoninvasive, preemptive medical tests. Currently, visual assessments,capability optimization services/apps, and medical testing all takeplace in separate settings, though disparate channels and providers.Accordingly, a need exists for systems and methods for providing a rangeof assessments and training options (e.g., for skills optimization ormedical treatment) from a single source.

In particular, the disclosed systems and methods recognize that eyetracking data may provide visual assessments, skill optimization, andmedical diagnostic testing and treatment. In view of the challengesoutlined above, the present disclosure provides systems and methods forconducting and generating visual, skills-based, and medical assessmentsbased on eye tracking data. Specifically, the disclosed systems andmethods enable communication between various physiological assessmentapplications that generate and collect user eye-tracking data. Theassessments may relate to different evaluation, training, or diagnosticpurposes. For example, one assessment may relate to improving a user'sathletic ability (e.g., training a tennis player to anticipate amovement) while another assessment may relate to diagnosing aneurological disorder. The disclosed systems and methods may provide abase of metrics computed from eye tracking data that may be commonacross the different assessments. In one embodiment, the communicationmay be provided via a hosting platform, as described in U.S. patentapplication Ser. No. 15/342,230 filed Nov. 3, 2016, the entiredisclosure of which is hereby incorporated by reference in its entirety.

The disclosed assessments may include any evaluations of eye movementdata, including assessments of a user's visual performance and/orassessments of a user's neurological state. Visual performance mayencompass all measures of a user's vision, e.g., a user's visual ability(e.g., a static ability that may be predetermined by the user's geneticcomposition), a user's visual capacity (e.g., abilities of a user thatmay be flexible or abilities that may vary with practice, training,therapy, etc.), a user's visual status (e.g., a user's eye movement andneurological responses at a given point in time), a user's eye movementresponse as a result of a user's neurological function, etc. In otherwords, visual performance may refer to a user's eye movement beingnormal (e.g., associated with a usual, healthy individual), above normal(e.g., an expert, professional-level, or experienced athlete, vehicledriver, hunter, etc.), or impaired (e.g., due to injury or aneurological disorder). Exemplary neurological disorders may include,for instance, Parkinson's Disease, Autism, Attention Deficit Disorder,stroke, Cerebral Palsy, Multiple Sclerosis, Dementia, etc. A user'sneurological state may encompass a user's neurological function at agiven point in time, including a user's progression in gain or loss ofneurological function related to eye movements and vision.

Turning now to the figures, FIG. 1 is a block diagram of an exemplaryphysiological assessment system 100 including a private cloud, backendenvironment (e.g., private assessment platform 101) and a user-facing,front-end environment (e.g., user environment 102). The privateassessment platform 101 may comprise a cloud environment hostingmultiple human physiological assessments relating to eye tracking data.In one embodiment, the assessment platform 101 may receive any eyemovement data, e.g., data collected from any eye tracking device (e.g.,via a web application system 113 to be discussed in further detail). Theassessment platform 101 may further convert received eye movement datato a format usable by any visual assessment application hosted by theassessment platform 101. For one embodiment, the assessment platform 101may analyze a set of foundational vision metrics, e.g., fixationstability, saccades, smooth pursuit, dynamic visual acuity, cardinalgaze position, reaction time, reading ability, etc.) to generateassessments of a user's physiology. In other words, the assessmentplatform 101 may collect and analyze foundational vision metrics togenerate conclusions on a user's physiology.

Assessment platform 101 may host a plurality of assessment applications(e.g., assessment processors 103 a-103 n), an algorithm processor 105, amodeling processor 107, a results processor 109, and a results database111. The assessment processors 103 a-103 n may have variousconfigurations and be associated with one or more applicationprogramming interfaces (APIs), as described in U.S. patent applicationSer. No. 15/342,230 filed Nov. 3, 2016, the entire disclosure of whichis hereby incorporated by reference in its entirety. In one embodiment,each assessment may exist as a set of algorithms. The assessmentplatform 101 may integrate each set of algorithms of each of the hostedassessments into the foundational metrics of the platform 101, e.g., viaalgorithm processor 105. The foundational metrics may then be used toprovide results for any assessments hosted on the assessment platform101. In other words, the algorithm processor 105 of the assessmentplatform 101 may provide continuity between several visual assessmentsand their respective eye tracking collection mechanisms andcalculations. The algorithm processor 105 may further evaluate userperformance across multiple visual assessment applications and providerecommendations for the user to improve, maintain, or rehabilitate hisor her visual performance. Additionally, the assessment platform 101 mayleverage data and analyses from multiple visual assessment applicationsto improve user performance evaluations and recommendations, e.g., viamodeling processor 107.

In one embodiment, algorithm processor 105 may determine a data schemafor each assessment hosted by the assessment platform 101. The algorithmprocessor 105 may further categorize eye tracking data of user(s) basedon the data schema. For example, algorithm processor 105 may index rawdata according to the data schema. Raw eye tracking data may include x,y, and/or z coordinates and/or calibration data directly measured orprovided by an eye tracker tracking a user's eye movement while takingan assessment. Alternately or in addition, algorithm processor 105 mayfurther index and categorize metrics (e.g., computed quantitative data).In this way, the algorithm processor 105 may facilitate data discoveryfor evaluating a given user or for building diagnostic/expected metricsfor each assessment. For example, for a selected assessment, algorithmprocessor 105 may access metrics categorized as being related to theassessment. The categorized metrics may then be used by modelingprocessor 107 to create “expected” or baseline metrics. Operations ofthe algorithm processor 105 may be observed and revised by support or ITpersonnel.

Categorized metrics may include categorical data that providesindications of a group to which collected raw data may belong.Categorized metrics provide a first step for further analysis (e.g., bymodeling processor 107). For example, age data may provide placecollected data in an age bracket, to later analyze whether age is afactor that influences results. The same is true for demographics dataincluding gender, ethnicity, handedness etc. Other examples ofcategorical data may include notes (e.g. clinical observations,including whether a patient wore eye glasses), feature data (e.g.,games, saccades, lights or stimuli being on or off, etc.), useridentifying data (e.g., participant, company, or assessmentidentifier(s), etc.), HIPAA data (e.g., name, date of birth, gender,etc.), survey data including input data from third parties (e.g.,entered by a patient/user or doctor, online survey services or websites,or backend assessment (e.g., assessment platform 101, trainer(s) andpersonnel), business analytics (data used to provide sales and customersuccess information on the customer usage, including medical codes),etc.

Categorical data may follow certain rules designated by the assessmentplatform 101 and/or component(s) of the web application system 113(e.g., protocol buffer 115). The rules may provide a form ofstandardization. Standardizing data across the platform 101 may providea known output understood by researchers, engineers, users, medicalprofessionals, etc. Standardization may provide interpretationdocumentation that reduces questions from such parties, allowing the rawdata and quantitative data (described in further detail below) to bebetter understood. Standardization may also reduce error in output andfacilitate interpretation and cleaning of the data. Further,standardization in categorization of data may provide guidance to allparts of the platform 101, including guidance on testing to follow inevaluating the accuracy of assessments and guidance on computation ofdata for interpretation. Standardization performed by the assessmentplatform 101 and/or the web application system 113 may additionallyinclude creating automated rules for consumption of data forinterpretation. The automated rules may facilitate and speed datainterpretation.

In one embodiment, algorithm processor 105 may use proprietaryalgorithms to compute metrics based on received raw eye tracking data.For example, one algorithm may include performing one or more stepsincluding grouping samples collected during a presentation of stimuli(of an assessment). For instance, an assessment may show stimulipositioned at various degree points of a screen, and raw eye trackingdata samples may be separated into groups according to the position ofthe stimuli presented. Alternately or in addition, an algorithm mayinclude checking data quality or filtering out certain data samples. Forinstance, collected data may entail some loss of data samples. Datacollection that fails to meet a certain quality (e.g., loss of samplesbeing less than 10%) may trigger a prompt for a user to re-take anassessment. Exemplary filtering of data samples may include filteringout and removing gaze data of zero from the collected data.

Algorithm processor 105 may also be configured to calculate gaze pointsfor each sample, e.g., sample of filtered and data quality-certifieddata. Gaze point calculation may include, for instance, averaging lefteye and right eye gaze data to find gaze data for both eyes of the usertaking the assessment. Alternately or in addition, algorithm processor105 may determine locations of samples. For example, for each sample,algorithm processor 105 may determine the location of the sample inrelation to the target band (within 9 mm), 1st error band (within 9-18mm), 2nd error band (within 18-36 mm) on the left or right side of ascreen displaying a selected assessment (of assessment processors 103),or outside of these bands on the left or right side of the screen.

Calculations of velocities may also be performed, e.g., finding thevelocity associated with each sample using a distance/time calculation.Again, the samples used to calculate velocities may also be sample(s) offiltered and data quality-certified data. Algorithm processor 105 mayalso calculate fast and slow phase velocity for each stimulus. Forexample, for each sample during the stimuli presentations, if the eyesare within an error radius, and if the eyes move past the stimulus andperform a saccade to return to the target, the algorithm processor 105may record the velocity as a fast phase movement. If eyes are movingtoward the stimulus within the error radius and have not moved past thestimulus, algorithm processor 105 may note the eye movement as a slowphase. For each of the fast and slow phase velocities for each stimuli,algorithm processor 105 may then calculate the mean (or average) fastphase and slow phase velocity for each stimuli and the associatedstandard deviation (SD).

Using velocity and distance, and timing thresholding, algorithmprocessor 105 may also categorize sample(s) a saccade, smooth pursuit,or fixation. For example, samples with velocities greater than30°/second may be marked as saccades. For samples that are not labeledas saccades, contiguous samples may be grouped together if they meetpredetermined distance and timing thresholds. This grouping may belabeled as fixations. Remaining samples may be labeled as smoothpursuit. Saccade, smooth pursuit, and fixation percentages may becalculated based on the time each eye movement occurred over the time ofanalysis.

Algorithm processor 105 may also compute On Target, Predictive, andLatent Smooth Pursuit Percentage(s). For example, for each samplelabeled as a smooth pursuit, that sample may be labeled as an on targetsmooth pursuit sample if the sample is within a predetermined distancethreshold from the stimulus when the samples was recorded. Of thesampled eye tracking data that fall outside the distance threshold butwithin a given error radius, samples that are ahead of the stimulus atthe time the sample was recorded may be labeled/categorized aspredictive smooth pursuit samples, and samples behind the stimulus atthe time the sample was recorded may be categorized as latent smoothpursuit samples. Samples outside of this error radius may not beconsidered to be tracking the stimulus and are therefore not consideredin these metrics.

Algorithm processor 105 may also compute eye target velocity error bytaking the difference of the sample's velocity with the stimuli'svelocity, for each sample. The average across samples may be a user'starget velocity error. Algorithm processor 105 may further computesynchronization. First, the instantaneous phase for the horizontal (x)and vertical (y) component of the eye tracking samples as well as thestimuli's coordinates during the assessment may be computed. Next, thephase difference between the eye and the stimuli may be computed. Fromthis, the synchronization value can be determined by finding theintensity of the first Fourier mode.

Algorithm processor 105 may also be configured to determine scoring. Forexample, for each sample, algorithm processor 105 may determine thedirection (left or right) moved and the furthest point the eyes made onthat side, using computed location information. If the detected locationof the eyes fall within a target region/band, a score may be assignedbased on the region. If the location of the eye(s) fall in an errorband, algorithm processor 105 may determine if the eye(s) moved passedthe vertical midpoint of that band. If they did, the algorithm processor105 may categorize the eye movement as an overshot. Otherwise, the eyemovement may be categorized as an undershot. Eye locations that falloutside all of the target bands may be categorized as misses. Saccadesmay be categorized as the number of times the user traveled from oneside of the assessment screen to the other. Fixations may be a categoryfor any data showing stopping points, or anytime the user hit a target,error band, or missed a target.

The modeling processor 107 may provide computations based on thecategorized eye tracking data. For example, modeling processor 107 mayaggregate data received from multiple users to develop norms for eachdata schema, including determining data that characterizes expected,healthy, or normal conditions. Modeling processor 107 may develop normsby analyzing the categorized data. For instance, modeling processor 107may select variable(s) for each assessment and employ various dataanalysis techniques, including feature reduction, supervised learning,outlier detection, object recognition, of a combination thereof. Themodeling processor 107 may perform one or more data analysis operationsfor each assessment with supervision, or automatically. In oneembodiment, the modeling processor 107 may produce calculated data.Calculated data may include qualitative data and quantitative data.

Qualitative data may be represented in numbers or described in words.Qualitative data may be received from surveys (asking people forfeedback or health status) or clinical notes (made by healthcare ormedical professionals). Quantitative data may include processednumerical data collected by the eye tracker and categorized by theplatform 101. Quantitative data may be hierarchical in platform 101.Previously described raw data and categorical data may be consideredquantitative data. The modeling processor 107 may further computequantitative data using the raw data and categorical data. Exemplarycomputed quantitative data may include value(s) for metric(s) (e.g.,smooth pursuit percentage), model data, model data, comparative data(e.g., percentiles, comparisons of gaze paths, functional bandwidth,etc.), indicators and scores (e.g. binocular scores), confidence data(e.g., reliability in computed metric(s), calibration for head movement,percentage of samples lost, data collection issues, etc.), normativedata, age-based norms, skill level norms, etc.

Results processor 109 may compare a selected user's eye tracking data tothe norms developed by modeling processor 107 and provide reports orresults for the assessment taken by a user. At the same time, the eyetracking data may be used to compute or supplement the development ofnorms for other assessments. For example, assessment platform 101 maystore eye tracking data from a user taking an assessment to improve herathletic ability, and the eye tracking data may be used to providebaseline data for an Alzheimer's assessment. In one embodiment, theresults database 111 may store the processed results of multiple usersand assessments in the data schema designated by the algorithm processor105.

In generating assessments for a particular user, the results processor109 may produce data visualizations from aggregated data and computedvalues of metrics. For example, results processor 109 may draw fromprior, stored results relating to the particular user as well as priorusers. The prior users may have taken the same assessment as theparticular user, or different assessment(s). As long as the prior users'data is stored by the data schema, the modeling processor 107 may userthe prior users' data to generate norms or expected results for aselected assessment that the particular user took, and the resultsprocessor 109 may evaluate the particular user using the generatednorms. Results database 111 may aid in collective analysis of aplurality of users. Stored data from the results database 111 may beused to improve the data collection and/or analytics of platform 101.Results database 111 may include a database of formatted data and/orcomputed values of one or more metrics. Alternately or in addition,platform 101 may include a cache (not shown) including real-time or rawdata.

The data of platform 101 may be viewed as falling into six levels ofdata that build on one another. Level 1 data may include raw eyetracking data (e.g., x, y, and z coordinates). Level 2 data may includefiltered raw data. For example, raw data may be filtered to removeanomalies, e.g., high velocity reflections. Level 2 data may provide thebasis for valid calculations. Level 1 and level 2 data may provide afoundation on which to build further calculations (e.g., regardingfixations, saccades, smooth pursuits, etc.) Level 3 data may includeclassified eye movement data. The classified data may identifyoculomotor events. Level 4 data may then take the base calculations ofoculomotor data and, using formulas (e.g., of algorithm processor 105),calculate groups of data or compute one or more values of metric(s). Forexample, for the metric of smooth pursuit, level 4 data may provide apercentage of time a user's gaze is in smooth pursuit. Level 5 data mayuse level 1, 2, and 3, data to make calculations across groups ofindividuals/users. For example, level 5 data may include a smoothpursuit percentage of a particular user compared to all othertest-takers of an assessment. Level 5 metrics may comprise model data,including data for data mining, cleaning, or feature reduction foridentifying an outcome, e.g., identification of far-sightedness ornear-sightedness. Level 6 data may include metrics that permitinterpretation of data for a particular user, e.g., actually identifyingif the user is near-sighted or far-sighted. Level 6 data may allow theresults processor 109 to provide results based on the user's assessmentresponses and the responses of population(s).

Physiology evaluation application programming interface (API) 119 mayprovide the interface between the backend private cloud environment 101and a user-facing (front-end) user environment 102. API 119 may beassociated with a storage service or database for raw eye tracking datareceived from a user taking an assessment. The database may store raweye tracking data, filter the raw data, classify eye movement detectedin the raw data, and store data schema that may map raw data to one ormore metric(s). API 119 may further include database(s) of formatteddata, including computed metric(s) for the assessment and/or raw dataformatted in a data type that may be used for analysis by algorithmprocessor 105, modeling processor 107, and/or results processor 109.Alternately or in addition, formatted data may include calibrated dataor data that meets a pre-set quality threshold for the selected visualassessment. The database(s) of formatted data may further include modeldata (e.g., data relating to norms/population data for a particularassessment) or interpretations of metric(s).

The user-facing frontend environment 102 provides a user with access tothe assessment platform 101. User-facing environment 102 may include aweb application system 113 and a web application portal 117. A user mayprovide eye tracking data via the web application system 113 and receiveassessment results via the web application portal 117.

In one embodiment, users may take assessments via a web applicationsystem 113. The web application system 113 may be comprised of asoftware application installed on a device including or interfacing withan eye tracker, e.g., a Tobii® device (not shown). Physiologicalassessments of assessment processors 103 a-103 n may be displayed on ascreen of the device operating web application system 113. The devicemay include any type of electronic device configured to send and receivedata, e.g., a mobile device, smartphone, personal digital assistants(“PDA”), tablet computer, or any other kind of touchscreen-enableddevice, a personal computer, a laptop, and/or server. The device may befurther associated with peripheral devices, for example, button presses,joysticks, headsets, virtual reality consoles, etc. Each device may havea web browser and/or mobile browser installed for receiving anddisplaying electronic content received from API 119 and/or webapplication portal 117. The device may further include eye trackingcapabilities and include wearable or remote camera(s), sensors, webcams,video cameras, remote eye trackers, mobile phones, tablet computers,spectacles, visors, helmets, implanted devices, contact lenses, or anyother detection mechanisms that aid in the detection of human eyes, thelocation and/or orientation of the human eyes, detection of bodyposition of the user, and/or the location and/or orientation of thewearable cameras and/or remote cameras.

The assessments may include various prompts or exercises for a user'seyes, including various visual cues for a user to look at one or moreobjects, etc. In one embodiment, web application system 113 may providean interface or display comprising a listing of assessments provided byassessment processors 103 a-103 n. A user may select one or moreassessments from the interface of web application system 113, e.g., viaa “Select a Test” menu comprising a list of assessments and a checkboxnext to each assessment. In one embodiment, web application system 113may present a text describing an assessment if a user hovers a mouse,pointer, gaze, or other selection mechanism over the respectiveassessment. The web application system 113 may then retrieve, generate,or access a webpage and JavaScript code of the selected assessment(s)from servers of assessment platform 101 and display the assessmentinterface(s) to the user. The data for test selection may be pulled fromAPI 119, which may read the data from the database. The device operatingthe web application system 113 may collect raw eye tracking data fromthe user's response to the assessment. Raw data may refer to datadirectly measured by an eye tracker of the device operating the webapplication system 113. For example, raw data may include x, y, and/or zcoordinates and/or calibration data (including calibration data for theeye tracker of the device running the web application system 113 andmeasuring the user's eye movement).

The web application system 113 may then send the raw eye tracking datato the assessment platform 101 via the physiology evaluation API 119.Personal health information data collected through the web applicationsystem 113 may be encrypted and stored via a Health InsurancePortability and Accountability Act (HIPAA)-compliant storage platform.The web application system 113 may retrieve eye tracking data from thedevice via a subscriber pattern, e.g., a callback function. For example,the callback function of the web application system 113 may be invokedeach time the device eye tracker captures an eye coordinate. Thecoordinate may then be saved with a timestamp.

In one embodiment, the web application system 113 may include orinteract with a protocol buffer, e.g., protocol buffer 115. The protocolbuffer 115 and/or the assessment platform 101 (e.g., API 119 or any ofthe various processors of the assessment platform 101) may define a dataschema of physiological assessment metrics and raw eye tracking data.One benefit of providing the data schema and protocol buffer is thateach metric of each assessment of the assessment processors 103 a-103 nmay have clear definitions of each metric. For example, a metric mayinclude measure(s) of fixations, saccades, and smooth pursuits. The dataschema of the protocol buffer 115 and assessment platform 101 mayprovide consistent definitions on how fixation stability, saccades,smooth pursuit, dynamic visual acuity, cardinal gaze position, reactiontime, reading ability, etc. are determined or measured. For instance,the data schema may dictate that a fixation may be measured as aninstance where a user's eye maintains a gaze in a single location for aselected time period, e.g., at least 200 ms. The exemplary data schemamay further define the minimum movement that an eye may take before itis considered not relatively stationary, and therefore performing asaccade.

The data schema further has the benefit of enforcing and clarifying thedata type used for each metric, e.g., timestamps for a fixation metricand eye tracking paths for a smooth pursuit metric. The data type foreach metric may further include defining which metrics to use for eachassessment. For example, an autism assessment or sports trainingassessment may rely on smooth pursuit metrics, and an ADHD assessmentmay primarily use fixation and microsaccade-related metrics. Eachassessment may have its own data schema, and each data schema may be setupon introducing or adding each assessment processor 103 a or 103 n tothe assessment platform 101. Adding an assessment processor 103 n to theassessment platform 101 may further include defining the metric(s) thatthe assessment of the assessment processor 103 n may determine. Forexample, an ADHD assessment may help to collect raw eye tracking datathat assessment platform 101 may use to compute various fixation andmicrosaccade-related metrics. The computed fixation andmicrosaccade-related metrics may then be used to evaluate a user's ADHD.

In one embodiment, assessment results may be retrieved through webapplication portal 117. The portal 117 may provide access to reportsdetailing results of the assessment(s) and provide tracking of theuser's progress. Portal 117 and results processor 109 may generate allor part of such reports. In one embodiment, portal 117 may be accessedfrom a browser. For example, results of each assessment taken by a givenuser may be computed and stored at platform 101. Once a user logs intoportal 117, the user may access the stored results of each assessment.

Users of the present embodiments may include any people desiring toassess and/or improve, maintain, or rehabilitate their health andwellness (e.g., in the form of detecting or diagnosing possibleneurological impairment/disorder and/or evaluating or improving motorskills, cognition, and/or kinesiology. For example, users may includeany individuals who perform physical activities that require observationand/or decision making ahead of physical and/or mental action. Theseusers can include athletes, pilots, drivers, heavy machine operators,lab equipment technicians, physicians, law enforcement professionals,and/or any other individuals seeking performance enhancement orindividuals involved in actions that require a cognitive process inorder to respond more effectively and efficiently. Alternately or inaddition, users may include individuals wanting to ensure that theirvision is healthy. Alternatively or in addition, users may includelearning or cognitively impaired individuals seeking to improve,maintain, or rehabilitate their mental and physical abilities. Platform101 may be used as a diagnostics tool in a school screening context, asa home-administered consumer screening platform, as a government orlocal screening platform at a community facility (e.g., a pharmacy,clinic, prison, military post, etc.), as a healthcare-affiliatedscreening platform, etc.

Exemplary recommendations output by platform 101 may include suggestingthat a user see a healthcare provider, providing a notification to ahealthcare provider of a user's visual performance abilities/status(e.g., for a provider to monitor a user's disease status or progressfrom treatment), etc. The recommendations may include real-time reportsthat deliver assessment results and therapies/training based on theresults. For instance, the reports may include graphical visualizationsof granular and important data for a user and/or healthcare provider.

It should be appreciated that physiological assessment system 100 mayinclude any type or combination of computing systems, e.g., handhelddevices, personal computers, servers, clustered computing machines,and/or cloud computing systems. In one embodiment, physiologicalassessment system 100 may be an assembly of hardware, including amemory, a central processing unit (“CPU”), and/or one or more userinterfaces. The memory may include any type of RAM or ROM embodied in aphysical storage medium, such as magnetic storage including floppy disk,hard disk, or magnetic tape; semiconductor storage such as solid statedisk (SSD) or flash memory; optical disc storage; or magneto-opticaldisc storage. The CPU may include one or more processors for processingdata according to instructions stored in the memory. The functions ofthe processor may be provided by a single dedicated processor or by aplurality of processors. Moreover, the processor may include, withoutlimitation, digital signal processor (DSP) hardware, or any otherhardware capable of executing software. The one or more user interfacesmay include any type or combination of input/output devices, such as adisplay monitor, touchpad, touchscreen, microphone, camera, keyboard,and/or mouse, or other interface.

Physiological assessment system 100 may be run over a network 121, withvarious components of physiological assessment system 100 runningremotely or independently from one another. Alternately or in addition,physiological assessment system 100 may run locally on a device orsystem, e.g., without Internet access. The network may include theInternet, a content distribution network, or any other wired, wireless,and/or telephonic or local network. In one embodiment, components ofplatform 101 may communicate via a first network. Assessment platform101 and user environment 102 may then communicate over a second network,different from the first network. In one embodiment, the first networkand/or the second network may include a private network.

The components of the assessment platform 101 may communicate through arepresentational state transfer (REST) protocol. When there is an error,a log file of each system component may show where the error took place.The platform 101 may further include an automated test tool that may totest the interface between API 119, web application system 113, and webapplication portal 117 to ensure that data format(s) are correct. Thearrangements and operations of the components (e.g., processors,devices, databases, etc.) of FIG. 1 show only one embodiment of thephysiological assessment system 100. The operations may be performed byany component or arrangement of components.

FIG. 2A is a flow diagram of an exemplary method 200 for generating adata schema for a physiological assessment, according to an exemplaryembodiment of the present disclosure. In one embodiment, method 200 maybe performed by API 119 and/or algorithm processor 105. Step 201 mayinclude determining a plurality of visual assessments, e.g., as offeredby assessment processors 103 a-103 n. Step 203 may include determining,for each assessment of the plurality of visual assessments, a dataschema. At least one data schema may be associated with more than oneassessment of the plurality of visual assessments. In other words, step203 may include defining a way to organize raw eye tracking data acrossdisparate assessments. Each data schema may be associated with one ormore metrics, e.g., fixation stability, saccades, smooth pursuit,dynamic visual acuity, cardinal gaze position, reaction time, readingability. Step 203 may further include defining a data type for eachmetric. For example, the metric of fixation stability may be related todata types pertaining to timestamp(s) and saccades. As another example,the metric of smooth pursuit may be defined as being related to datatype(s) for any data conveying a gaze path. Step 205 may include storingthe determined data schema, e.g., using algorithm processor 105. Step207 may include receiving user eye tracking data associated with aselected visual assessment of the plurality of visual assessments andstep 209 may include determining, of the stored data schema, a selectedstored data schema associated with the selected visual assessment. Forexample, steps 207 and 209 may include determining that a selectedassessment evaluates a user based on a fixation(s) observed in theuser's raw eye tracking data. Fixations may be the stored data schemaassociated with the selected assessment. Steps 207 and 209 may theninvolve detecting fixations in the received raw eye tracking data, e.g.,detecting where the user's eye tracking data involves a gaze at astationary location for at least 200 ms. Step 211 may includecategorizing the received eye tracking data based on the selected storeddata schema. Following from the previous example, step 211 may includeisolating the fixations detected in the user's eye tracking data andstoring the isolated fixations in the data schema of “fixations.”

FIG. 2B is a flow diagram of an exemplary method 220 for generating aphysiological assessment based on eye tracking data, according to anexemplary embodiment of the present disclosure. Method 220 may beperformed by API 119, modeling processor 107, and/or results processor109. Step 221 may include receiving a metric to compute, the metricrelating to the selected visual assessment. Step 223 may includereceiving categorized raw user eye tracking data (e.g., from step 211 ofmethod 200). Step 225 may include receiving stored data relating to themetric, e.g., from modeling processor 107 and/or results database 111.The stored data may be associated one or more individuals other than theuser taking the selected visual assessment or providing the raw eyetracking data. Step 227 may include computing quantitative data based onthe categorized data and data related to one or more individuals otherthan the user. For example, step 227 may include computing a value ofthe metric (of step 221) based on the received categorized data of theindividual and the received data associated with individual(s) otherthan the user. Computing the quantitative data may include filteringreceived user eye tracking data to remove data anomalies. Thequantitative data may further be computed from an analysis of multiplemetrics. For example, an ADHD assessment may be built from both saccadesand smooth pursuit data. In this way, step 227 may include computing avalue of a second metric of the one or more defined metrics, the secondmetric being different from the first metric (e.g., the metric of step211).

Multiple metrics may also entail metrics relating to the user andanother user/test subject. For example, the first metric may be based onthe user eye tracking data and the second metric may be based on eyetracking data related to an individual other than the user. Thequantitative data may be computed based on the computed value of thefirst metric and the computed value of the second metric. Populationdata may be generated based on the computed value of the first metricand the computed value of the second metric, where the population datarelates to a test subject other than the user and the individual otherthan the user. In other words, population data may relate to one or morenorms (or expected values for a population of users), for each metric.

Step 229 may include generating a report of user physiological functionbased on the computed quantitative data and/or outputting the report toa web portal (e.g., portal 117). The report may include population data,e.g., from modeling processor 107 For instance, the report may include acomparison of the user's physiological capabilities (e.g., as shown froma computed value of a metric related to the first user) to the value ofthe metric as given by population data. The report of physiologicalfunction may include an assessment of the user's reading performance,athletic ability, or neurological function.

In operation, physiological assessment system 100 may detect involuntaryeye movement behavior to evaluate a user's physiology. A user's eyemovements may be prompted through various visual displays (stimuli) on ascreen. The stimuli may vary in size, speed, color, and movement for thepurpose of producing the following eye movement behaviors for each eyeseparately and a combined (both) eye result. The observed eye movementbehaviors may include saccades (e.g., fast eye movements), smoothpursuits (e.g., eye movements that follow an object), and fixations(e.g., stopping points of the eye). Exemplary physiologicalassessment(s) may include a reading assessment, a sports-related visionassessment, and an assessment of neurological function. Each of theassessments may provide outputs in the form of graphs, icons, charts,data tables, lists, comparisons to norms/averages, etc. The assessmentsmay further include recommendation to improve (e.g., reading, athleticability, or neurological function) based on the poorest results. Therecommendation may include vision therapy drills. Each of theassessments may further display an indicator of the results to aspecialist, e.g., an optometrist.

Exemplary Reading Assessment

The reading assessment may include a test displayed as a string, or inthe form of one or more paragraphs. In one scenario, the first paragraphmay appear automatically. When the user presses a button, the currentparagraph may be hidden, words per minute may be calculated andrecorded, and the next paragraph may be shown. If there is no additionalparagraph to show, the reading portion of the assessment may end. Theword count and timing of the user's reading (as determined fromcollected eye tracking data) may be processed for a words per minute(wpm) calculation. One embodiment may include prompting or entering aninitial reading level for the assessment, and appropriate content may beshown. After text is read, questions may be answered to gaugecomprehension. Different stimuli (e.g., numbers) may be shown to peoplewho have trouble reading for any number of reasons.

The reading assessment may be provided in various languages ordifficulty levels. Analysis of eye tracking data may be based on thefollowing factors:

Grade level of the text presented by the reading assessment;

Lines to analyze in the presented text (e.g., the total number oflines);

Lines found by the user (e.g., lines read or found by the user's gaze);

Lines found by analysis (e.g., return sweeps of a user's gaze per pageof text);

Age of the user at the time of testing;

Title of the text read;

Head movement percentage, e.g., the percentage of the movement done withhead versus eyes;

Vergence;

Recording Time, e.g., the amount of time useful data was recorded.Recording time may influence analysis reliability;

Analysis Reliability, e.g., the level of confidence in the reportmetrics and outcome (where n analysis reliability score lower than 80%may cause the system 100 to prompt the user to retake the assessment;

Dry Eye, e.g., whether the user has clinically determined dry eyes;

Visual Fatigue, e.g., the tiredness of the user's eyes;

Binocular Vision Issues, e.g., whether the eyes travel in similarpatterns;

Fixations/100 Words (#), e.g., a stopping point of the eyes;

Regressions/100 words (#), e.g., the number of times the user lookedbackwards (in English content this is from right to left), whilereading;

Average Fixation Duration (ms), e.g., the average amount of time thereader stopped (fixated) while reading;

Reading Rate (words/minute; #), e.g., how many words were read perminute of reading;

Regression/Fixation Ration (%);

Correct Comprehension Answers (%), e.g., the number of comprehensionquestions were answered correctly; and

Disparity Between Eyes (mm), e.g., the difference between the left andright eye taken at fixations then divided by the number of fixations.

Exemplary Sports-Related Vision and Training Assessment

The Sports-Related Vision and Training Assessment may include variousareas of vision that impact a user's athletic performance. Theassessment may be built from four types of metrics: functional,mechanics, mind-eye, and on-field. Functional metrics may refer to theability to have basic, fundamental visual health and functionality suchas acuity, contrast, dryness. Mechanics may refer to the ability of theeyes to work together, the muscle and nerve coordination to maintaineffective and efficient use of the eyes, etc. Mind-Eye may refer to theinterplay of vision and neuro-connectivity to include visual processing.On-Field may refer to how environmental factors can influenceperformance including vision via motor responses such as reactions,impulses and distractibility. The four types of metrics may beinter-dependent. For instance, having poor functional skills may alsoaffect a user's mechanics, mind-eye ability, or on-filed ability.

Percentiles may be given for each type of metric as the overall weightedpercentile. Percentiles may show where a given user stands compared totheir peers. For example, a score output by the assessment at 50thpercentile may mean that the user scored better than 50 out of every 100athletes at the user's designated skill level. The assessment output mayinclude may include a weighted score developed by optometrists, coaches,and sport vision experts. This score may be based on athleticperformance needs.

Norms for sports-related assessment data may be calculated acrossvarious categories, e.g., Elite, Professional, College, Elite Youth,Amateur, etc. The sports-related assessment may generate values for thefollowing metrics or types of eye movements: Circular Smooth Pursuit(CSP), Horizontal Smooth Pursuit (HSP), Vertical Smooth Pursuit (VSP),Horizontal Saccades (HS), Vertical Saccades (VS), Fixation Stability,Choice Reaction Time (CRT), Discriminate Reaction Time (DRT), Contrast,Static Visual Acuity (SVA), Dynamic Visual Acuity (DVA), etc. Thesports-related assessment may further evaluate a user based on thefollowing factors.

Static (Visual) Acuity, e.g., the ability to see stationary objectsclearly (20/20), such as reading a play book;

Dynamic (Visual) Acuity, e.g., the ability to see moving objects (objectsize fixed) clearly, such as tracking a ball or reading hand signals;

Dry Eye, which can affect visual clarity and fatigue;

Fixation Stability, e.g., the ability to keep the eyes stable and fixedon a certain spot for a period of seconds;

Fatigue, e.g., visual fatigue or tiredness of the eyes which can affectmany aspects of visual performance;

Mechanics, e.g., coordination between each eye, as well as muscle andnerve coordination to maintain effective and efficient use of the eyes;

Latency, e.g., the amount of time it takes the eyes to react when atarget is presented;

Targeting, e.g., the accuracy between the target and where a user's eyegazes;

Speed, e.g., the velocity of a user's eyes;

Binocular Vision Issue, e.g., a measure of the extent to which a user'seyes are “teaming” or working in unison or in coordination with eachother;

Mind-Eye metric(s), e.g., interplay of vision and neuro-connectivity toinclude visual processing, including, e.g., processing time (e.g., thetime it takes to “think” once something is seen), decision makingaccuracy (e.g., the outcome of the thought and whether a user's responseis correct or incorrect, reaction time (e.g., how quickly a userresponded to the task;

On-Field metric(s), e.g., how environmental factors can influenceperformance including vision via motor responses, including, e.g.,distractibility (e.g., a user's ability to pay attention to the task orassessment at hand), impulsivity (e.g., a user's ability to be“patient,” waiting for the information to present itself beforeresponding, etc.

The assessment may also be based on the following factors:

Contrast Sensitivity, which may measure visual function, for instance,in situations of low light, fog, glare, or in the sun). One scenario mayinclude when the contrast between, for example the white ball and thegrey clouds is reduced. A low ability in contrast may affect a players'performance on cloudy days or in night games.

Depth, which may include a measure of the point at which the eyesconverge compared to a point of focus. A negative number may mean thepoint of convergence is further than the required focus point. Apositive score may mean the point of convergence is closer than therequired focus point.

Tracking ability, which may measure the ability of both eyes working incoordination to follow an object, such as a ball. One way of expressingtracking ability may include a Smooth Pursuit Percentage in response tothe assessment.

Disparity, which may measure the difference between each eye whenlooking at a target. Disparity may include a measure of whether a user'seyes focus together, allowing the user to see clearly or whether theuser's eyes focus apart, affecting their depth perception.

Efficiency, which may measure the path a user's eyes take from onetarget to the other. Like running bases in baseball, efficiency measureswhether a user's eyes take the most efficient/shortest/most directpathway to view a target, or engage in extra motions that increase thetime it takes for the gaze to reach the next visual target of theassessment.

Speed (/Accuracy Trade-off) may refer to the trade-off that occursbetween a user moving their eyes quickly, while also being fast.Depending on the sport, a user may need one more than the other, or bothequally.

Exemplary Assessment of Neurological Function

The assessment of neurological function may evaluate brain health andfunction as reflected in oculomotor behavior. The assessment may includevarious measured aspects of vision, including oculo-motor function ineach eye (eye muscle function (e.g., overactive or underactive for eacheye), fixations, pursuits, and saccades. The assessment may furtherinclude: determining linkage(s) between the measured aspects of visionto brain function (or dysfunction), determining typical symptom(s) thatare experienced in a person (where an area of brain dysfunction isidentified), determining typical risk(s) that are associated with aperson's life (with the area of brain dysfunction identified),recommending specifically-targeted therapy to improve in the area of theidentified area of brain dysfunction, etc.

Exemplary Outcome categories may include the following: Normal (e.g., noindication of brain health issues based on reference model data), Notnormal (e.g., indication of abnormal behavior that is indicative of somebrain related issue based on a reference brain health model, which maybe different from other issues such as dry eye), Level of Normality(e.g., the statistical probability of a persons' level of normal to notnormal), etc. Additional categories include diagnoses or evaluations ofconcussion, multiple sclerosis, diabetes, dementia, and/or other visualand/or brain related processes.

Analytics Processes may include data collection, cleaning,transformation, formatting, clinical verification, modelling features,profiling, comparing profiles, providing results. Exemplary datamodeling and analytical models of the neurological function assessmentmay be created using various analytical processes and/or algorithms suchas logistical regression, information criterion, Gaussian Finite Method,Boolean approaches, principle component analysis, Cox Model, GradientBoosting model. Additional analysis may include confidence intervals,probabilities, ROC curves, Random Forest, sensitivity, specificity,precision, accuracy. Additional and ongoing monitoring and analytics mayinclude machine learning and may be programmed to mine data, makeprojections, and determine relationships. The analytics process may bederived from the following:

Score (provided in different units, e.g., millimeters, degrees etc.) mayinclude a user's score on a metric.

Percentiles may show where a user stands compared to othersusers/individuals. 50th percentile may mean a user scored better than 50out of every 100 people. For some assessments, a high percentile mayindicate brain health.

Smooth Pursuit Percentage may refer to eye movements that follow atarget/visual cue within a velocity range of the target/visual cue. Theeye movements may be reported as a percentage of the test time. For someassessments, a high smooth pursuit measurement may indicate brainhealth.

Efficiency (measured in millimeters) may refer to the error in a user's'gaze, relative to a reference or ideal pathway (that is the pathway thestimuli takes during testing). For some assessments, a low efficiencymeasurement may indicate brain health.

Variability (measured in millimeters) may refer to the average varianceof a user's response to an assessment, from a reference or idealpathway. Observed or received user data may include (or be evaluated toinclude) variance in three segments of the pathway, middle, left/rightor up/down.

Eccentric variability may refer to the variability in a user'sassessment response, on the left and right portions of the screen thatare outside central gaze position.

Recovery (measured in millimeters) may refer to the difference in auser's visual path taken before and after a fixation. A wide, loopingpath may be inefficient. A narrow path, where the eye moves withoutgreat loops, may be preferred. For some assessments, a low recoverymeasurement may indicate brain health.

Target Accuracy (measured in millimeters) may refer to a distance from atarget, including the distance each “hit” or fixation location was,compared to the ideal target location at a given time. For someassessments, a low target accuracy measurement may indicate brainhealth.

Speed may refer to the average velocity of a user's saccades across theduration of an assessment. For some assessments, a higher speedmeasurement may indicate brain health.

Pathway Length Differences (measured in millimeters) may refer to theaverage difference in distance between an eye gaze pathway of the rightversus an eye gaze pathway of the left eye. For some assessments, a lowpathway length difference may indicate brain health.

Distance (measured in millimeters) may refer to the average distance auser's gaze pathway is from an ideal or reference pathway. For someassessments, a low distance measurement may indicate brain health.

Depth (measured in millimeters) may refer to the difference between apoint of convergence of a user's eyes and the screen, as provided by aneye tracker. A negative number may indicate a point of convergencebehind the screen. A positive number may indicate a point of convergencein front of the screen.

Phoria may refer to deviation of the line of sight inward (eso+) oroutward (exo−). A user displaying no deviation (ortho) (e.g., a resultof zero) may have good brain health.

Tropia may refer to deviation of a user's line of sight, where one eyemay be above the other. If the tropia value is a negative value, theuser's left eye may be higher than the right. If the tropia value ispositive value, then the user's right eye may be higher than left. Atropia value close to zero may be preferred.

Other data related to amplitude, velocity, convergence, head movement,gaze spread, object recognition, gaze pathways, efficiency, variability,size, and others. Additional eye tracking metrics may incorporatefixations, saccades, smooth pursuits, square wave jerks, glissades,microsaccades and others. Raw data may also be used to analyze trends,develop additional metrics.

The assessment may include training and/or therapy recommendations. Thetraining or therapy may be linked to deficiencies and recommended astreatment to improve abnormalities. Various graphs may be used inreports to display metrics. Graphs may include line graphs, bar charts,pie charts for example. Alternately or in addition, reports may includevisualizations, e.g., gaze replays, real time analytical display ofresults and other visualizations. The report may include an exemplaryproprietary scale of Functional Status, providing analysis ranging froma broad interpretation of received data to a specific analysis. Forexample, a report may include the “scale of functional status.” Thisscale may provide a range of performance that goes from dysfunctional tofunctional to exceptional performance. In other words, the scale mayprovide a level of normality or functional status across all the eyemovement classifications and metrics. A user may then drill down intosegments of the report for more detail on each metric and how it relatesto neurological performance.

One exemplary scale of functional status of the report ranges from zeroto 100. Zero may represent complete dysfunction. 100 may representexceptionally good performance. “Functional performance,” which may liebetween dysfunctional and exceptional performance may show the averageperformance across a population. A user and doctor may determine wherethe user falls on the scale. For example, if a user is a professionalathlete looking to gain an edge, “functional performance” may not besufficient. The professional athlete user may strive to attainexceptional performance. On the other hand, if a user is an elderlyperson who is looking to determine if s/he can still drive, “functionalperformance” may be a sufficient level of performance.

The scale may be visualized in a report with changes in color from redto orange to yellow to green. In one embodiment, red may indicatedysfunctional performance and green may indicate performance in thefunctional and exceptional range. Ranges of “functional” may be adjustedin accordance with a user's age. In other words, the very young and veryold may have wider functional ranges than the middle aged. In oneembodiment, the functional range for a specific sport level or specialgroup can be calculated and reported for a customized range.

In one embodiment, report(s) may further include data related to rightand left eyes with the oculo-motor muscles. The oculo-motor muscles maywork to push and pull the eyes in different ways. If there is animbalance in these muscles, where one muscle is overactive, and/oranother muscle is underactive, pursuit tracking may be affected. Theirregularity in the pursuit tasks may be displayed in eye tracking datawhen odd shapes or patterns occur. The assessment of neurologicalfunction may measure these patterns in the vertical and horizontalsmooth pursuit tasks. The assessment may also identify, for example,“V”-shaped patterns, “A”-shaped patterns, “X”-shaped patterns, diamondpatterns, or rare patters in both or one eye. Once such a pattern isidentified, the assessment may show the likely muscles (e.g., in redcolor), affected by such a pattern. The muscles may be labeled under theimage. Furthermore, a description of the pattern as well as possiblesymptoms and risks may be displayed. The muscle imbalance may be linkedto a training module for specific rebalancing therapy.

The report may further include a stack of blocks that categorize one ormore major eye movements, fixations, pursuits, saccades, etc. A fixationmay provide a stopping point of the eye and form the base of vision andvisual stability. Pursuits may include eye movements that follow atarget, e.g., a ball or moving car. Saccades may include fast eyemovements that are designed to relocate our focal vision to an areawithin the vision field. Each block may include the labels, “My Score”and “percentile.” “My score” may refer to a weighted score the userattained from the metrics within that category. A weight may include thecontribution that metric made toward the overall score. The weight maybe developed through analytics that look to differentiate functionalfrom dysfunctional vision. The score may be compared to an overalldatabase including scores for people across the spectrum of age, gender,ethnicity etc. The comparison may provide a percentile. A percentile of80 may indicate that the user received a score higher than 80 out 100people in the database. A percentile score of 20 may indicate that auser received a score got higher than 20 out of 100 people in thedatabase. A higher percentile may be preferred, as the higher percentagemay indicate a higher functional status.

Based on a user's percentile, each major classification, e.g.,fixations, saccades, and pursuits, may be given a color. If a user'spercentile is low, the user may be given a red color for that eyeclassification. For example, if the user's fixation percentile was 10,the fixation block in the user's report may be colored red. If theuser's fixations were in the 90th percentile, the user's fixation blockmay be colored green. As there are more pursuit metrics and thosemetrics may have more weight than saccades metrics, and more saccademetrics that have more weight than fixation metrics, each block maycontribute on a different level to a user's overall score. The metricsmay be weighted differently based on the neurological functionassessment analytics model, that is they may contribute at differentrates compared to each other when determining if a user's eye movementsoverall are functional or dysfunctional.

The report may include an image of the brain, based on which area showsthe greatest dysfunction and has the highest weight. If a user's area ofgreatest dysfunction is fixations, then the major brain area affectedmay be displayed in the report as the brain stem. If a user's area ofgreatest dysfunction is pursuits, then the major brain areas affectedmay be displayed in the report as the parietal lobe and cerebellum. If auser's area of greatest dysfunction is saccades, then the major brainareas affected may be displayed in the report as the frontal lobe andcerebellum.

The colors of each block may be determined in an exemplary embodiment asfollows: If a user has two blocks that are red, e.g., saccades andpursuits, the report may show the brain region(s) related to pursuits asthose contribute more to one's overall health and function. A trainingmodule associated with the neurological function assessment may showexercises linked to a user's greatest deficiencies first. The report mayinclude written content below the images of the brain. This writtencontent may include a description of what major eye movement was mostdysfunctional, and how the movement is defined. The written content mayfurther include typical symptoms and risks associated with thisdysfunction. In one embodiment, the training exercises may be providedwith the written content. The training test(s) may link a user's highestdysfunction to a therapy exercise that can help the user improve theirperformance.

The report may include vertical tests, including vertical tracking orpursuits and vertical speed and targeting or saccades. The report mayinclude a ledger at the bottom of the page indicating what various theacronyms mean e.g., “SP”=smooth pursuit. The second page of the reportmay include definitions of all the metrics on the report. The report mayinclude an image of the user facing forward show the perspective of agaze replay. When online, the user may press play to see any of thesegaze patterns in motion. The left and in the bottom corner of the reportmay include horizontal speed and targeting or saccades, the report mayalso include the horizontal tracking which is related to pursuits, andthen stability which is related to fixations. The report may furtherinclude, at the top, a display indicating circular smooth pursuit. Eachof this test data may be presented in any variety of orders orconfigurations. The above description includes only one exemplary formatfor the report.

The platform 101 may perform the following exemplary assessment stepsfor eye muscles. First, trends in pattern tracing may be hundreds ofdifferent data sets, e.g., using modeling processor 107 of platform 101.The data sets may be compared to known clinical outcomes to determine alevel of match or correspondence. Mathematical equations may be createdthat capture the patterns that are matched with clinical outcomes. Alevel of accuracy may be determined between the mathematical equationsand the verified clinical results until a minimum predetermined accuracythreshold is acquired. The level of severity of a user's deviation fromthe level of accuracy may be determined based on thresholding. (Numbersfrom a model/normal/expected data set may be used for the computation ofthe level of severity.) The platform may further link dysfunction (givenby the level of severity) to muscles. For a report, the severity may belinked to a color code, e.g., to provide a color-codedvisualization/display by the severity of dysfunction.

The platform 101 may perform the following exemplary assessment stepsfor brain regions. For example, brain regions may be categorized intovarious eye movement categories (e.g., fixations, saccades, andpursuits). Platform 101 (e.g., modeling processor 107) may The platform101 may compare received user eye tracking data sets to known clinicaloutcomes to determine a level of match. Platform 101 may further createmathematical equations that capture the patterns that are matched withclinical outcomes. A level of accuracy between the mathematicalequations and the verified clinical results may be determined until aminimum predetermined accuracy threshold is acquired. A logisticalregression may be conducted to determine weighted contributions of themetrics and overall weights may be calculated for each major eyeclassification (e.g., fixation, saccade, pursuit). The platform 101 mayalso determine the probability of normality (dysfunctional, functional,and exceptional) for each individual, compared to the model populationdata set. The platform 101 may further determine the level ofdysfunction for each eye classification (e.g., fixation, saccade,pursuit, etc.), link dysfunction to brain image, and for the purposes ofthe report, link severity to color, e.g., to produce a color-codedvisualization of the severity of dysfunction. The platform 101 mayfurther link content of symptoms and risks based on the greatest eyemovement classification dysfunction and generate a detailed descriptionof exemplary measured aspects of vision and possible linkage(s) betweenthe measured aspects of vision to brain function/dysfunction.

In summary, the disclosed systems and methods provide a range offormerly silo-ed and disparate assessments and training options at asingle source. To do so, the disclosed systems and methods recognizethat eye tracking data may provide visual assessments, skilloptimization, and medical diagnostic testing and treatment. Thedisclosed systems and methods therefore enable communication betweenvarious physiological assessment applications that generate and collectuser eye-tracking data. Although the assessments relate to differentfunctions, the disclosed systems and methods provide a platform that cancompute outcomes for each of the different functions, using the commonbase of eye tracking data.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A method for assessing user physiology via eyetracking data, the method including: determining a plurality of visualassessments; determining, for each assessment of the plurality of visualassessments, a data schema, where at least one data schema is associatedwith more than one assessment of the plurality of visual assessments;storing the determined data schema; receiving user eye tracking dataassociated with a selected visual assessment of the plurality of visualassessments; determining, of the stored data schema (a), a selectedstored data schema associated with the selected visual assessment;categorize the received eye tracking data based on the selected storeddata schema; computing quantitative data based on the categorized dataand data related to one or more individuals other than the user;generating a report of user physiological function based on the computedquantitative data; and outputting the report to a web portal.
 2. Themethod of claim 1, further comprising: defining one or more metrics foreach data schema; and defining a data type for each metric of the one ormore metrics.
 3. The method of claim 2, further comprising: filteringthe received user eye tracking data to remove data anomalies; andcomputing a value of a first metric of the one or more defined metricsbased on the filtered eye tracking data, where the computed quantitativedata includes the computed metric.
 4. The method of claim 3, furthercomprising: computing a value of a second metric of the one or moredefined metrics, the second metric being different from the firstmetric; and computing the quantitative data based on the computed valueof the first metric and the computed value of the second metric.
 5. Themethod of claim 4, wherein the computed value of the first metric isbased on the user eye tracking data and the computed value of the secondmetric is based on eye tracking data related to an individual other thanthe user.
 6. The method of claim 5, further comprising: generatingpopulation data based on the computed value of the first metric and thecomputed value of the second metric, where the population data relatesto a test subject other than the user and the individual other than theuser.
 7. The method of claim 6, further comprising: generating thereport by comparing the computed value of the first metric to thegenerated population data.
 8. The method of claim 1, wherein the reportof physiological function includes an assessment of the user's readingperformance, athletic ability, or neurological function.
 9. A system forassessing user physiology via eye tracking data, the system including: adata storage device storing instructions for hosting one or more visualassessments; a processor configured to execute the instructions toperform a method including: determining a plurality of visualassessments; determining, for each assessment of the plurality of visualassessments, a data schema, where at least one data schema is associatedwith more than one assessment of the plurality of visual assessments;storing the determined data schema; receiving user eye tracking dataassociated with a selected visual assessment of the plurality of visualassessments; determining, of the stored data schema (a), a selectedstored data schema associated with the selected visual assessment;categorize the received eye tracking data based on the selected storeddata schema; computing quantitative data based on the categorized dataand data related to one or more individuals other than the user;generating a report of user physiological function based on the computedquantitative data; and outputting the report to a web portal.
 10. Thesystem of claim 9, wherein the processor is further configured for:defining one or more metrics for each data schema; and defining a datatype for each metric of the one or more metrics.
 11. The system of claim10, wherein the processor is further configured for: filtering thereceived user eye tracking data to remove data anomalies; and computinga value of a first metric of the one or more defined metrics based onthe filtered eye tracking data, where the computed quantitative dataincludes the computed metric.
 12. The system of claim 11, wherein theprocessor is further configured for: computing a value of a secondmetric of the one or more defined metrics, the second metric beingdifferent from the first metric; and computing the quantitative databased on the computed value of the first metric and the computed valueof the second metric.
 13. The system of claim 12, wherein the computedvalue of the first metric is based on the user eye tracking data and thecomputed value of the second metric is based on eye tracking datarelated to an individual other than the user.
 14. The system of claim13, wherein the processor is further configured for: generatingpopulation data based on the computed value of the first metric and thecomputed value of the second metric, where the population data relatesto a test subject other than the user and the individual other than theuser.
 15. The system of claim 14, wherein the processor is furtherconfigured for: generating the report by comparing the computed value ofthe first metric to the generated population data.
 16. The system ofclaim 9, wherein the report of physiological function includes anassessment of the user's reading performance, athletic ability, orneurological function.
 17. A computer readable medium storinginstructions that, when executed by a computer, cause the computer toperform a method of assessing user physiology via eye tracking data, themethod including: determining a plurality of visual assessments;determining, for each assessment of the plurality of visual assessments,a data schema, where at least one data schema is associated with morethan one assessment of the plurality of visual assessments; storing thedetermined data schema; receiving user eye tracking data associated witha selected visual assessment of the plurality of visual assessments;determining, of the stored data schema (a), a selected stored dataschema associated with the selected visual assessment; categorize thereceived eye tracking data based on the selected stored data schema;computing quantitative data based on the categorized data and datarelated to one or more individuals other than the user; generating areport of user physiological function based on the computed quantitativedata; and outputting the report to a web portal.
 18. The computerreadable medium storing instructions of claim 17, the method furthercomprising: defining one or more metrics for each data schema; anddefining a data type for each metric of the one or more metrics.
 19. Thecomputer readable medium storing instructions of claim 18, the methodfurther comprising: filtering the received user eye tracking data toremove data anomalies; and computing a value of a first metric of theone or more defined metrics based on the filtered eye tracking data,where the computed quantitative data includes the computed metric. 20.The computer readable medium storing instructions of claim 19, themethod further comprising: computing a value of a second metric of theone or more defined metrics, the second metric being different from thefirst metric; and computing the quantitative data based on the computedvalue of the first metric and the computed value of the second metric.